Details for communication and engagement activities and materials along with the time line and success indicators. It includes a record of communication activities that have been undertaken during the first half of the project, and those planned for the second period.
A document describing the first version of the platform architecture, how the architecture meets the (initial) requirements of the federated and privacy-preserving machine learning services designed in WP 4.1, how it addresses the (initial) user stories developed in WP7, and how it aligns with existing Industrial Data Platform standards.
Report containing an exhaustive list of domain-specific and business requirements coming from the two scenarios and from other complementary domains, and assessment of available datasets. Report containing a complete specification of technical requirements to drive technical developments in WPs 3,4,5,6 and WP7 integration.
Detailed description of the technical and domain business-specific KPIs that will be used for validating the MUSKETEER data platform.
A document describing the main functionalities of the client connector. It will contain the design of the component and how it interact with services at server side. This is the first version of the report.
A document describing the final version of the platform architecture, how it meets the final requirements of the federated and privacy-preserving machine learning services designed in WP 4.1, how it addresses the final user stories developed in WP7, how it supports incorporating active security measures against adversarial attacks (data poisoning, evasion), and how it aligns with existing Industrial Data Platform standards.
A technical report containing the final structure of the privacy operation modes describing how every algorithm will operate over these modes and the details about the SW architecture and design patterns that will facilitate future extensions of the SW library.
A report describing the main threats and vulnerabilities that may be present in federated machine learning algorithms considering both, attacks at training and test time and defining requirements for the design, deployment and testing of federated machine learning algorithms. This report would also form a strong basis from which governance and or legislative input could be drawn.
his task aims to detail the strategy for dissemination of project results through appropriate channels and during various stages of the project. The underlying goal of dissemination activities will to ensure public awareness of the project and promote interest. This task will ensure practical dissemination using the different instruments mentioned in WP8.
A document describing the main common evaluation framework. It will contain the design of the different tests and datasets that will be used in the evaluation, as well as the merit performance measurements to be obtained.
MUSKETEER public website, to be active and regularly updated during the whole project and maintained for 1 year following the project’s completion. The MUSKETEER factsheet will be an early leaflet for dissemination and communication purposes, including the most relevant information of the project in a nutshell, and will be available from the very beginning.
A demonstration (and report) of a first prototype of the MUSKETEER platform, demonstrating the end-to-end execution of data sharing and federated machine learning for synthetic data and at least one use case, supporting privacy operating modes POM1-POM3. Includes demonstration of basic dashboard reporting.
This deliverable is in the form of software will present Version 0 of the library covering a set of pre-processing steps needed before using the machine learning algorithms. Models for the estimation of the data value will also be provided.
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Author(s): Greg Collinge, Emil C Lupu, Luis Muñoz-González
Published in: ESANN 2019 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Issue Year., 2019, Page(s) 43-48
Author(s): A. Navia-Vazquez M. Vazquez-Lopez J. Cid-Sueiro
Published in: FL-ICML 2020 : International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020, 2020